In the contemporary landscape of convergence between Information Technology (IT) and Operational Technology (OT), maintaining asset visibility within the Industrial Control Systems (ICS) infrastructure is paramount. Yet, the escalating targeting of legacy systems and air-gapped networks by cyber threats underscores the need for robust security measures and solutions to counter unauthorized access and malicious activities. This paper presents an analysis aimed at identifying gaps and opportunities in integrating IT Security Solutions with OT environments. These solutions offer comprehensive features such as real-time threat detection, asset identification, network monitoring, and incident response capabilities tailored specifically for the unique challenges posed by industrial control networks. Leveraging the Festo MPS 403–1 system as a case study, we delve into the intricacies of IT/ OT integration and highlight areas necessitating improvement to achieve seamless convergence and harness the advantages of unified systems across industries.

In the rapidly expanding landscape of Internet of Things (IoT), sensors have emerged as pivotal components, playing a critical role in sensing and data collection. However, this crucial function also renders them susceptible to potential threats such as unauthorized access and security breaches. This paper investigates the vulnerabilities that lead to these threats and offers a comprehensive set of best practices aimed at fortifying the security of IoT devices. We highlight key measures such as secure device design, robust authentication mechanisms, encrypted communication protocols, regular updates, and emphasize the importance of collaborative efforts among stakeholders using an ontological approach. An Ontology provides a structured framework for organizing knowledge, facilitating clearer communication, efficient data management, and enhanced decision-making. Thus, this paper contributes to the development of a more secure and resilient IoT ecosystem.

In response to the growing need for compliance with diverse safety and security regulations, crucial for safeguarding both humans and machines in the era of Industry 4.0 (I4.0), it has also become increasingly crucial for industries to ensure adherence to safety and security standards. However, navigating the complexities of regulations and standards can be daunting, often leading to inefficiencies in compliance processes. Our goal is to foster a comprehensive understanding of an effective approach to meeting compliance requirements in these related fields. Therefore, this paper introduces a semi-automated method for ensuring compliance with unified safety and security standards within the context of I4.0. This approach empowers management teams to thoroughly analyze the specific requirements necessary to maintain a protected environment. Compliance can be achieved at both the asset component and system levels, aligning with the relevant standards. Additionally, we present a detailed analysis of a use case and its output, demonstrating the practical application and efficacy of our approach.

To make a management decision for the Operational Technology (OT) environment, obtaining OT asset information from a plant is essential. Key Performance Indicators (KPIs) can play a crucial role in evaluating the performance of the manufacturing process, minimizing production costs, assessing process effectiveness, and investigating various settings to improve performance, operations, and quality in OT. This paper aims to provide a detailed examination of how Asset Information Mining Sources (AIMSs) support the process for acquiring both static and runtime OT asset information. Once we have obtained accurate OT asset information, we can calculate the KPIs and incorporate them into a knowledge representation using an ontology-based model. This model can assist the management team in making informed decisions and provide a roadmap for exploring further automation possibilities.

The current industry has to adapt to rapidly changing customers' needs. Reconfigurable manufacturing, therefore, provides capacity and functionality on demand which is essential for competitiveness in fast-changing markets. Furthermore, Industry 4.0 or even more so, Industry 5.0 emphasizes human-centred production with collaborative robots, Cobots, to create human-robot interactions. In such scenarios, safety and security are difficult to address due to the intrinsic features of reconfigurable manufacturing, like exposure to numerous requirements changes in a short period. As safety and security can conflict in different phases of the system life-cycle, one of the earliest activities to avoid conflicts is requirements engineering which can significantly diminish the cost and time of fixing issues compared to later phases like operation. This paper proposes a methodology for safety and security requirements interaction management, including conflict detection and resolution, and shows its applicability through a reconfigurable collaborative human-robot use case. Based on the proposed methodology, we detected and resolved two safety and security requirement conflicts.

Today’s industrial control systems consist of tightly coupled components allowing adversaries to exploit security attack surfaces from the information technology side, and, thus, also get access to automation devices residing at the operational technology level to compromise their safety functions. To identify these concerns, we propose a model-based testing approach which we consider a promising way to analyze the safety and security behavior of a system under test providing means to protect its components and to increase the quality and efficiency of the overall system. The structure of the underlying framework is divided into four parts, according to the critical factors in testing of operational technology environments. As a first step, this paper describes the ingredients of the envisioned framework. A system model allows to overview possible attack surfaces, while the foundations of testing and the recommendation of mitigation strategies will be based on process-specific safety and security standard procedures with the combination of existing vulnerability databases.